import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE 		=      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE 		=      {
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """encoder.layer_norm_for_extract""": """layer_norm_for_extract""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """quantizer.weight_proj""": """quantizer.weight_proj""",
    """quantizer.vars""": """quantizer.codevectors""",
    """project_q""": """project_q""",
    """final_proj""": """project_hid""",
    """w2v_encoder.proj""": """lm_head""",
    """label_embs_concat""": """label_embeddings_concat""",
    """mask_emb""": """masked_spec_embed""",
    """spk_proj""": """speaker_proj""",
}
_SCREAMING_SNAKE_CASE 		=      [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """label_embeddings_concat""",
    """speaker_proj""",
    """layer_norm_for_extract""",
]





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	List[Any]:







 '''simple docstring'''
 for attribute in key.split("""."""       ):
  UpperCamelCase			  =      getattr(UpperCamelCase_							,					UpperCamelCase_       )

 if weight_type is not None:
  UpperCamelCase			  =      getattr(UpperCamelCase_							,					UpperCamelCase_       ).shape
 else:
  UpperCamelCase			  =      hf_pointer.shape

 if hf_shape != value.shape:
  raise ValueError(
      f"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
      f""" {value.shape} for {full_name}"""       )

 if weight_type == "weight":
  UpperCamelCase			  =      value
 elif weight_type == "weight_g":
  UpperCamelCase			  =      value
 elif weight_type == "weight_v":
  UpperCamelCase			  =      value
 elif weight_type == "bias":
  UpperCamelCase			  =      value
 else:
  UpperCamelCase			  =      value

 logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""       )





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_       )		->	int:







 '''simple docstring'''
 UpperCamelCase			  =      []
 UpperCamelCase			  =      fairseq_model.state_dict()

 UpperCamelCase			  =      hf_model.unispeech_sat.feature_extractor

 for name, value in fairseq_dict.items():
  UpperCamelCase			  =      False
  if "conv_layers" in name:
   load_conv_layer(
       UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					hf_model.config.feat_extract_norm == """group"""							,					)
   UpperCamelCase			  =      True
  else:
   for key, mapped_key in MAPPING.items():
    UpperCamelCase			  =      """unispeech_sat.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
    if key in name or key.split("""w2v_model."""       )[-1] == name.split("""."""       )[0]:
     if "layer_norm_for_extract" in name and (".".join(name.split("""."""       )[:-1]       ) != key):
      # special case since naming is very similar
      continue
     UpperCamelCase			  =      True
     if "*" in mapped_key:
      UpperCamelCase			  =      name.split(UpperCamelCase_       )[0].split("""."""       )[-2]
      UpperCamelCase			  =      mapped_key.replace("""*"""							,					UpperCamelCase_       )
     if "weight_g" in name:
      UpperCamelCase			  =      """weight_g"""
     elif "weight_v" in name:
      UpperCamelCase			  =      """weight_v"""
     elif "bias" in name:
      UpperCamelCase			  =      """bias"""
     elif "weight" in name:
      # TODO: don't match quantizer.weight_proj
      UpperCamelCase			  =      """weight"""
     else:
      UpperCamelCase			  =      None
     set_recursively(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )
    continue
  if not is_used:
   unused_weights.append(UpperCamelCase_       )

 logger.warning(f"""Unused weights: {unused_weights}"""       )





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	Dict:







 '''simple docstring'''
 UpperCamelCase			  =      full_name.split("""conv_layers."""       )[-1]
 UpperCamelCase			  =      name.split("""."""       )
 UpperCamelCase			  =      int(items[0]       )
 UpperCamelCase			  =      int(items[1]       )

 if type_id == 0:
  if "bias" in name:
   if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
    raise ValueError(
        f"""{full_name} has size {value.shape}, but"""
        f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""       )
   UpperCamelCase			  =      value
   logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""       )
  elif "weight" in name:
   if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
    raise ValueError(
        f"""{full_name} has size {value.shape}, but"""
        f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""       )
   UpperCamelCase			  =      value
   logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""       )
 elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
  if "bias" in name:
   if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
    raise ValueError(
        f"""{full_name} has size {value.shape}, but"""
        f""" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found."""       )
   UpperCamelCase			  =      value
   logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""       )
  elif "weight" in name:
   if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
    raise ValueError(
        f"""{full_name} has size {value.shape}, but"""
        f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""       )
   UpperCamelCase			  =      value
   logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""       )
 else:
  unused_weights.append(UpperCamelCase_       )





@torch.no_grad()
def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_=None							,					UpperCamelCase_=None							,					UpperCamelCase_=True       )		->	str:







 '''simple docstring'''
 if config_path is not None:
  UpperCamelCase			  =      UniSpeechSatConfig.from_pretrained(UpperCamelCase_       )
 else:
  UpperCamelCase			  =      UniSpeechSatConfig()

 UpperCamelCase			  =      """"""

 if is_finetuned:
  UpperCamelCase			  =      UniSpeechSatForCTC(UpperCamelCase_       )
 else:
  UpperCamelCase			  =      UniSpeechSatForPreTraining(UpperCamelCase_       )

 UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      fairseq.checkpoint_utils.load_model_ensemble_and_task(
     [checkpoint_path]							,					arg_overrides={"""data""": """/""".join(dict_path.split("""/"""       )[:-1]       )}       )
 UpperCamelCase			  =      model[0].eval()

 recursively_load_weights(UpperCamelCase_							,					UpperCamelCase_       )

 hf_wavavec.save_pretrained(UpperCamelCase_       )


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE 		=      argparse.ArgumentParser()
     parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
     parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
     parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
     parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
     parser.add_argument(
         """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
     )
     _SCREAMING_SNAKE_CASE 		=      parser.parse_args()
     convert_unispeech_sat_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
     )

import unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
     import jax
@require_flax
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase  ,     unittest.TestCase		):
   __lowerCAmelCase           =				FlaxAutoencoderKL







   @property
   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    UpperCamelCase			  =      4
    UpperCamelCase			  =      3
    UpperCamelCase			  =      (32, 32)

    UpperCamelCase			  =      jax.random.PRNGKey(0				)
    UpperCamelCase			  =      jax.random.uniform(lowerCamelCase_   ,   ((batch_size, num_channels) + sizes)				)

    return {"sample": image, "prng_key": prng_key}







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      {
        """block_out_channels""": [32, 64],
        """in_channels""": 3,
        """out_channels""": 3,
        """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
        """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
        """latent_channels""": 4,
    }
    UpperCamelCase			  =      self.dummy_input
    return init_dict, inputs_dict
